{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# App dataset with decision trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import utils\n",
    "from sklearn import tree\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import random\n",
    "random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Platform</th>\n",
       "      <th>Age</th>\n",
       "      <th>App</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>15</td>\n",
       "      <td>Atom Count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>25</td>\n",
       "      <td>Check Mate Mate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Android</td>\n",
       "      <td>32</td>\n",
       "      <td>Beehive Finder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>35</td>\n",
       "      <td>Check Mate Mate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Android</td>\n",
       "      <td>12</td>\n",
       "      <td>Atom Count</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Android</td>\n",
       "      <td>14</td>\n",
       "      <td>Atom Count</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Platform  Age              App\n",
       "0   iPhone   15       Atom Count\n",
       "1   iPhone   25  Check Mate Mate\n",
       "2  Android   32   Beehive Finder\n",
       "3   iPhone   35  Check Mate Mate\n",
       "4  Android   12       Atom Count\n",
       "5  Android   14       Atom Count"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_dataset = pd.DataFrame({\n",
    "    'Platform': ['iPhone','iPhone','Android','iPhone','Android','Android'],\n",
    "    'Age': [15, 25, 32, 35, 12, 14],\n",
    "    'App': ['Atom Count', 'Check Mate Mate', 'Beehive Finder', 'Check Mate Mate', 'Atom Count', 'Atom Count']})\n",
    "app_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# With age as a categorical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Platform_iPhone</th>\n",
       "      <th>Platform_Android</th>\n",
       "      <th>Age_Young</th>\n",
       "      <th>Age_Adult</th>\n",
       "      <th>App_Atom_Count</th>\n",
       "      <th>App_Beehive_Finder</th>\n",
       "      <th>App_Check_Mate_Mate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Platform_iPhone  Platform_Android  Age_Young  Age_Adult  App_Atom_Count  \\\n",
       "0                1                 0          1          0               1   \n",
       "1                1                 0          0          1               0   \n",
       "2                0                 1          0          1               0   \n",
       "3                1                 0          0          1               0   \n",
       "4                0                 1          1          0               1   \n",
       "5                0                 1          1          0               1   \n",
       "\n",
       "   App_Beehive_Finder  App_Check_Mate_Mate  \n",
       "0                   0                    0  \n",
       "1                   0                    1  \n",
       "2                   1                    0  \n",
       "3                   0                    1  \n",
       "4                   0                    0  \n",
       "5                   0                    0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_dataset_one_hot = pd.DataFrame(\n",
    "    {'Platform_iPhone':[1,1,0,1,0,0],\n",
    "     'Platform_Android':[0,0,1,0,1,1],\n",
    "     'Age_Young':[1,0,0,0,1,1],\n",
    "     'Age_Adult':[0,1,1,1,0,0],\n",
    "     'App_Atom_Count':[1,0,0,0,1,1],\n",
    "     'App_Beehive_Finder':[0,0,1,0,0,0],\n",
    "     'App_Check_Mate_Mate':[0,1,0,1,0,0]})\n",
    "app_dataset_one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = app_dataset_one_hot[['Platform_iPhone','Platform_Android','Age_Adult','Age_Young']]\n",
    "y = app_dataset_one_hot[['App_Atom_Count','App_Beehive_Finder','App_Check_Mate_Mate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X,y)\n",
    "dt.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(200.88000000000002, 181.2, 'X[3] <= 0.5\\ngini = 0.407\\nsamples = 6\\nvalue = [[3, 3]\\n[5, 1]\\n[4, 2]]'),\n",
       " Text(133.92000000000002, 108.72, 'X[1] <= 0.5\\ngini = 0.296\\nsamples = 3\\nvalue = [[3, 0]\\n[2, 1]\\n[1, 2]]'),\n",
       " Text(66.96000000000001, 36.23999999999998, 'gini = 0.0\\nsamples = 2\\nvalue = [[2, 0]\\n[2, 0]\\n[0, 2]]'),\n",
       " Text(200.88000000000002, 36.23999999999998, 'gini = 0.0\\nsamples = 1\\nvalue = [[1, 0]\\n[0, 1]\\n[1, 0]]'),\n",
       " Text(267.84000000000003, 108.72, 'gini = 0.0\\nsamples = 3\\nvalue = [[0, 3]\\n[3, 0]\\n[3, 0]]')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(dt, rounded=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# With age as a numerical feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Platform_iPhone  Platform_Android  Age              App\n",
      "0                1                 0   15       Atom Count\n",
      "1                1                 0   25  Check Mate Mate\n",
      "2                0                 1   32   Beehive Finder\n",
      "3                1                 0   35  Check Mate Mate\n",
      "4                0                 1   12       Atom Count\n",
      "5                0                 1   14       Atom Count\n"
     ]
    }
   ],
   "source": [
    "app_dataset = pd.DataFrame({\n",
    "    'Platform_iPhone':[1,1,0,1,0,0],\n",
    "    'Platform_Android':[0,0,1,0,1,1],\n",
    "    'Age': [15, 25, 32, 35, 12, 14],\n",
    "    'App': ['Atom Count', 'Check Mate Mate', 'Beehive Finder', 'Check Mate Mate', 'Atom Count', 'Atom Count']})\n",
    "print(app_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age  Platform_iPhone  Platform_Android\n",
      "0   15                1                 0\n",
      "1   25                1                 0\n",
      "2   32                0                 1\n",
      "3   35                1                 0\n",
      "4   12                0                 1\n",
      "5   14                0                 1\n"
     ]
    }
   ],
   "source": [
    "features = app_dataset[['Age','Platform_iPhone','Platform_Android']]\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         Atom Count\n",
      "1    Check Mate Mate\n",
      "2     Beehive Finder\n",
      "3    Check Mate Mate\n",
      "4         Atom Count\n",
      "5         Atom Count\n",
      "Name: App, dtype: object\n"
     ]
    }
   ],
   "source": [
    "labels = app_dataset['App']\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "app_model = DecisionTreeClassifier()\n",
    "app_model.fit(features, labels)\n",
    "app_model.score(features, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(133.92000000000002, 181.2, 'X[0] <= 20.0\\ngini = 0.611\\nsamples = 6\\nvalue = [3, 1, 2]'),\n",
       " Text(66.96000000000001, 108.72, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0, 0]'),\n",
       " Text(200.88000000000002, 108.72, 'X[1] <= 0.5\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
       " Text(133.92000000000002, 36.23999999999998, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(267.84000000000003, 36.23999999999998, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(app_model, rounded=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
